{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Copy train/tests folders\n",
    "To avoid uploading large file size, the train, test1, and test2 folders are removed from our submissions.\n",
    "Please kindly help to copy the folder train, test1, and test1 to this project folder (same location as this file)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependency Installations\n",
    "Please refer to the file `requirements.txt` for the list of packages used in this program. Some of the specific machine learning libraries, there will be codes regarding installations. However, for those common libraries (such as numpy, matplotlib, etc), we assumed that already available."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instructions\n",
    "Please run the following sections starting from Splitting data until before the Training.\n",
    "If you would like to continue the training process please consider increase the `max_iter` key in the configuration variable. If you do not want to run the training, it just run through the train section, it will skip the training since we've done the training.\n",
    "\n",
    "Most of the data was pre-processed so will execute quickly. Those take longer time there was the time recorded using %timeit to let the users know how long it may take."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detectron2 installations\n",
    "Make sure to install detectron2 and its dependencies by enabling the following lines if you haven't done so.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# # install dependencies:\n",
    "# !pip install pycocotools>=2.0.1\n",
    "# import torch\n",
    "# print(torch.__version__, torch.cuda.is_available())\n",
    "# !gcc --version\n",
    "# # Make sure also to install opencv (assumed to be pre-installed)\n",
    "# # install detectron2: (Colab has CUDA 10.1 + torch 1.6)\n",
    "# # See https://detectron2.readthedocs.io/tutorials/install.html for instructions\n",
    "# assert torch.__version__.startswith(\"1.6\")\n",
    "# !pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.6/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The stratify is by country and then by the damage type with lowest number of occurences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    " %load_ext autotime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 963 ms\n"
     ]
    }
   ],
   "source": [
    "from data_processing import split_train_data, process_tests, load_train_eval_splits, load_tests\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Records for train split exists\n",
      "Records for evaluation split exists\n",
      "time: 375 µs\n"
     ]
    }
   ],
   "source": [
    "# Process train/evaluation splits\n",
    "split_train_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test1 exists\n",
      "test2 exists\n",
      "time: 1.06 ms\n"
     ]
    }
   ],
   "source": [
    "# Process tests\n",
    "process_tests([\"test1\", \"test2\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 111 ms\n"
     ]
    }
   ],
   "source": [
    "train_dicts, eval_dicts = load_train_eval_splits()\n",
    "test1_dicts, test2_dicts = load_tests()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10974 1221 2631 2664\n",
      "time: 975 µs\n"
     ]
    }
   ],
   "source": [
    "print(len(train_dicts), len(eval_dicts), len(test1_dicts), len(test2_dicts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration\n",
    "See split balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 194 ms\n"
     ]
    }
   ],
   "source": [
    "from vis_utils import plot_damage_distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 : 5923\n",
      "1 : 3992\n",
      "2 : 7513\n",
      "3 : 5044\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 129 ms\n"
     ]
    }
   ],
   "source": [
    "train_counts = plot_damage_distributions(train_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5923, 3992, 7513, 5044]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.91 ms\n"
     ]
    }
   ],
   "source": [
    "train_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 : 669\n",
      "1 : 454\n",
      "2 : 868\n",
      "3 : 583\n"
     ]
    },
    {
     "data": {
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     "metadata": {
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     "output_type": "display_data"
    },
    {
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     "text": [
      "time: 101 ms\n"
     ]
    }
   ],
   "source": [
    "eval_counts = plot_damage_distributions(eval_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[669, 454, 868, 583]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.29 ms\n"
     ]
    }
   ],
   "source": [
    "eval_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "time: 116 ms\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format =\"svg\"\n",
    "# plt.rcParams['figure.dpi'] = 310\n",
    "fig, ax = plt.subplots()\n",
    "bar_width = 0.35\n",
    "index = np.arange(0, 8, 2)\n",
    "\n",
    "train_plt = ax.bar(index, train_counts, bar_width, label=\"Train\", color='darkgrey', hatch=\"////\", edgecolor='black')\n",
    "eval_plt = ax.bar(index + bar_width, eval_counts, bar_width, label=\"Evaluation\", color='dimgrey',  edgecolor='black')\n",
    "\n",
    "ax.set_xlabel('Damage Type')\n",
    "ax.set_ylabel('Damage Count')\n",
    "ax.set_title('Damage Type Train/Evaluation Distributions')\n",
    "ax.set_xticks(index + 0.5*bar_width)\n",
    "ax.set_xticklabels(['D00', 'D10', 'D20', 'D40'])\n",
    "\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "\n",
    "ax.legend()\n",
    "plt.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the area distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 182 ms\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "train_annos = []\n",
    "for train_dict in train_dicts:\n",
    "    train_annos += copy.deepcopy(train_dict['annotations'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "22472"
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.35 ms\n"
     ]
    }
   ],
   "source": [
    "len(train_annos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 36.9 ms\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "bboxes = np.array([anno['bbox'] for anno in train_annos])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 9.7 ms\n"
     ]
    }
   ],
   "source": [
    "damage_types = np.array([anno['category_id'] for anno in train_annos])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.74 ms\n"
     ]
    }
   ],
   "source": [
    "areas = (bboxes[:, 2]-bboxes[:, 0])*(bboxes[:, 3]-bboxes[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22472"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.84 ms\n"
     ]
    }
   ],
   "source": [
    "len(areas)"
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  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.35 ms\n"
     ]
    }
   ],
   "source": [
    "sizes = np.sqrt(areas)"
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  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22472"
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     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.99 ms\n"
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   "source": [
    "len(sizes)"
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   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 832 µs\n"
     ]
    }
   ],
   "source": [
    "# Plot the area distribution\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 135 ms\n"
     ]
    }
   ],
   "source": [
    "index = np.arange(20)\n",
    "fig, ax = plt.subplots()\n",
    "ret = ax.hist(sizes, bins=20, rwidth=0.9, color='darkgrey', edgecolor='black')\n",
    "ax.set_xlabel('Area size')\n",
    "ax.set_ylabel('Area count')\n",
    "ax.set_title('Bounding box areas distributions (histogram, bins=20)')\n",
    "# ax.set_xticks(index + bar_width)\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "print(ret)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we will choose the sizes as [32, 64, 128, 256]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.44 ms\n"
     ]
    }
   ],
   "source": [
    "widths = bboxes[:, 2] - bboxes[:, 0]\n",
    "heights = bboxes[:, 3] - bboxes[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.69 ms\n"
     ]
    }
   ],
   "source": [
    "widths.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([12039]),)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.73 ms\n"
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   ],
   "source": [
    "np.where(widths==0)"
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   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64),)"
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     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
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  {
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   "execution_count": 49,
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     "text": [
      "time: 2.34 ms\n"
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    "# ratio is H/W\n",
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     "execution_count": 50,
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  {
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   "execution_count": 51,
   "metadata": {},
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     "execution_count": 51,
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 135 ms\n"
     ]
    }
   ],
   "source": [
    "index = np.arange(30)\n",
    "fig, ax = plt.subplots()\n",
    "ret = ax.hist(ratios, bins=30, rwidth=0.9, color='darkgrey', edgecolor='black')\n",
    "ax.set_xlabel('Ratio (height/width)')\n",
    "ax.set_ylabel('Ratio count')\n",
    "ax.set_title('Bounding box ratios distributions (histogram, bins=30)')\n",
    "# ax.set_xticks(index + bar_width)\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "print(ret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.54974109"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.05 ms\n"
     ]
    }
   ],
   "source": [
    "0.5959698 - 0.04622871"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we will choose 0.1, 0.5, 1.0, 1.5"
   ]
  }
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